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IBM IBM SPSS Modeler Professional(R)

down load the authoritative e book: Cloud Computing 2019: the use of the Cloud for competitive capabilities

See the entire record of computing device researching SolutionsSee consumer studies of IBM Watson Studio

final analysis

Watson is an umbrella for all IBM deep learning and synthetic intelligence, as well as machine studying. The business changed into a pioneer in introducing AI applied sciences to the business world. What this capability for consumers: Watson Studio is a accurate contender for any firm trying to deploy machine discovering and deep studying technologies.

The platform gives huge equipment and technologies for statistics scientists, developers and area matter experts that desire to explore facts, build fashions, and educate and set up computer discovering fashions at scale. The solution contains equipment to share visualizations and results with others. Watson Studio supports cloud, laptop and native deployment frameworks.

The latter resides at the back of a company’s firewall or as a SaaS solution operating in an IBM deepest cloud. IBM Watson Studio is ranked as a “chief” in the Forrester Wave. It became a valued clientele’ option 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a set of IBM equipment and technologies to construct effective machine getting to know applications and services. This comprises IBM Cloud pretrained laptop discovering models reminiscent of visual awareness, Watson natural Language Classifier, and others. The ambiance uses Jupyter Notebooks along with different open source tools and scripting languages to enhance developed-in collaborative challenge aspects.

The outcome is an environment that facilitates speedy and powerful machine getting to know development and satisfactory tuning of fashions. information scientists and others can choose between a variety of capacities of Anaconda, Spark and GPU environments.

Most fundamental statistics and file codecs are supported via open source Jupyter Notebooks.

Integration

IBM Watson Studio connects a number of IBM items, including SPSS Modeler and statistics Science journey (DSX) together with open supply equipment, in order to carry a robust Predictive Analytics and computer getting to know (PAML) answer.

The ambiance accommodates open facts sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version aspects interactivity with workstation servers and R Studio, along with Python, R., and Scala coder for information scientists.

Reporting and Visualization

Visualization through SPSS Modeler. strong logging and reporting features are developed into the product.

Refer RStudio launch tutorial. This documentation aims RStudio with venture and it is not yet purchasable in all statistics centers.

working Spark programs from RStudio

RStudio makes use of the brand new sparklyr package (http://spark.rstudio.com/index.html) to connect with the Spark kernel gateway on the cloud the use of Spark-as-a-service interactive APIs. The sparklyr kit includes a dplyr interface to Spark facts frames as well as an R interface to Spark’s allotted machine gaining knowledge of pipelines.

which you could use your latest Spark circumstances from RStudio. to make use of this characteristic, run the following steps:

1 Load and screen purchasable Spark cases

2 hook up with a Spark instance

three Run dplyr APIs and Spark’s disbursed computer discovering libraries

These data are created below your domestic directory, /domestic/rstudio. If the working directory is different from the home listing, which you could reproduction the config.yml and .RProfile data to your existing working directory.

you could load and monitor Spark situations by using the load_spark_kernels() and display_spark_kernels() R capabilities . illustration:

This function lists simplest your at the moment attainable Spark instances. if you want a further Spark illustration, create it in Watson Studio Environments.

connect with the selected Spark illustration

To connect with Spark, run the spark_connect R feature. as an example:

sc <- spark_connect(config = kernels[1])

After this Spark context is created, all subsequent operations might be executed the use of this Spark instance:

that you would be able to discover the illustration R script info in the /ibm-sparkaas-demos folder under your domestic listing. These examples display scenarios which you can run with Spark in RStudio.

spark-kernel-primary.R

Creates primary R statistics frames and generates far off Spark records frames in line with the native R information frames. also runs some basic filters and DBI queries.

sparkaas_mtcars.R

masses the normal mtcars R data frame after which generates a Spark statistics body for the mtcars statistics body. It then does transformations to create a practicing facts set and runs a linear model on the training information set.

Mahesh Kurapati is an Advisory utility Engineer with the IBM Analytics crew. Mahesh’s primary focal point is on the building of numerous micro-capabilities for IBM statistics Science journey. Mahesh is concerned within the building of a lot of sparkling.statistics elements and SparkaaS integration with RStudio. With more than 20 years of event in utility building, Mahesh has contributed key functionalities to IBM items together with SPSS information, SPSS Modeler, and SPSS Analytics Server.

at first posted at datascience.ibm.com on September 28, 2016.

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IBM SPSS Modeler Professional(R) Certification

Download the authoritative guide: Cloud Computing 2019: Using the Cloud for Competitive Advantage

See the full list of Machine Learning SolutionsSee user reviews of IBM Watson Studio

Bottom Line

Watson is an umbrella for all IBM deep learning and artificial intelligence, as well as machine learning. The company was a pioneer in introducing AI technologies to the business world. What this means for buyers: Watson Studio is a top contender for any organization looking to deploy machine learning and deep learning technologies.

The platform provides extensive tools and technologies for data scientists, developers and subject matter experts that desire to explore data, build models, and train and deploy machine learning models at scale. The solution includes tools to share visualizations and results with others. Watson Studio supports cloud, desktop and local deployment frameworks.

The latter resides behind an organization’s firewall or as a SaaS solution running in an IBM private cloud. IBM Watson Studio is ranked as a “Leader” in the Forrester Wave. It was a Customers’ Choice 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a collection of IBM tools and technologies to build powerful machine learning applications and services. This includes IBM Cloud pretrained machine learning models such as Visual Recognition, Watson Natural Language Classifier, and others. The environment uses Jupyter Notebooks along with other open source tools and scripting languages to complement built-in collaborative project features.

The result is an environment that facilitates fast and powerful machine learning development and fine tuning of models. Data scientists and others can choose from various capacities of Anaconda, Spark and GPU environments.

Most major data and file formats are supported through open source Jupyter Notebooks.

Integration

IBM Watson Studio connects several IBM products, including SPSS Modeler and Data Science Experience (DSX) along with open source tools, in order to deliver a robust Predictive Analytics and Machine Learning (PAML) solution.

The environment accommodates open data sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version features interactivity with Notebook servers and R Studio, along with Python, R., and Scala coder for data scientists.

Reporting and Visualization

Visualization through SPSS Modeler. Strong logging and reporting functions are built into the product.

No result found, try new keyword!the average analytics professional reports primary use of five tools – the leaders being, open source statistical programming language R (which is supported by Alteryx), IBM SPSS Statistics, the SAS p...

In Part 1 of this series, we wrote about our goal to explore a use case and use various machine learning platforms to see how we might build classification models with those platforms to predict flight cancellations. Specifically, we hoped to predict the probability of the cancellation of flights between the ten U.S. airports most affected by weather. We used historical flight data and historical weather data to make predictions for upcoming flights.

In Part 2, we started our exploration with IBM SPSS Modeler and APIs from The Weather Company. With this post, we look at IBM’s Watson Studio.

Tools used in this use case solution

Watson Studio is a collaborative platform for data scientists, built on open-source components and IBM added value, which is available in the cloud or on-premise. In the simplest terms, Watson Studio is a managed Apache Spark cluster with a Notebook front-end. By default, it includes integration with data tools like a data catalog and data refinery, Watson Machine Learning services, collaboration capability, Model Management, and the ability to automatically review a model’s performance and refresh/retrain the model with new data — and IBM is quickly adding more capabilities. Read here to see what IBM is doing lately for data science.

A python notebook solution

In this case, we followed roughly the same steps we used in the SPSS model from Part 2, only this time we wrote python code in a Jupyter notebook to get similar results. We encourage readers to come up with their own solutions. Let us know. We’d love to feature your approaches in future blog posts.

The first step of the iterative process is gathering and understanding the data needed to train and test our model. Since we did this work for part 2, we made use of the analysis here.

Flights data — We gathered data for 2016 flights from the US Bureau of Transportation Statistics website. The website allowed us to export one month at a time, so we ended up with twelve csv (comma separated value) files. Importing those as dataframes and merging into a single dataframe was straightforward.

Weather data — With the latitude and longitude of the 10 Most Weather-Delayed U.S. Major Airports, we used one of the Weather Company’s API’s to get the historical hourly weather data for all of 2016 for each of the 10 airport locations and created a csv file that became our data set in the notebook.

Combined flights and weather data — To each flight in the first data set, we added two new columns: ORIGIN and DEST, containing the respective airport codes. Next, we merged flight data and the weather data so that the resulting dataframe contained the flight data along with the weather for the corresponding Origin and Destination airports.

Data preparation, modeling, and evaluation

To start preparing the data, we used the combined flights and weather data from the previous step and performed some cleanup. We deleted columns of features that we didn’t need, and replaced null values in rows where flight cancellations were not related to weather conditions.

Next, we took the features we discovered when we created a model using SPSS (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions) and we used them as inputs to our python model. We also chose the target feature for the model to predict: the cancellation status. We deleted the remaining features.

Next, we ran OneHotEncoder on the four categorical features. One-hot encoding is a process by which categorical features get converted into a format that works better with certain algorithms, like classification and regression. Figure 2 shows the number of feature columns, expanded significantly with one hot encoding.

Figure 2 — One-hot encoding expands 4 feature columns into many more

Interestingly, the flight data is heavily imbalanced. Specifically, as seen in Figure 3, of all the flights in the data set only a small percentage are actually cancelled.

To address that skewedness in the original data, we tried oversampling the minority class, under sampling the majority class, and a combination of both — but none of these approaches worked well. We then tried something called SMOTE (Synthetic Minority Over-Sampling Technique), an algorithm that provides an advanced over-sampling algorithm to deal with imbalanced datasets. Since it generates synthetic examples rather than just using replication, it helped our selected model work more effectively by mitigating the problem of overfitting that random oversampling can cause. SMOTE isn’t considered effective for high dimensional data, but that isn’t the case here.

In Figure 4, we notice a balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

Figure 4 — Distribution of cancelled and non-cancelled flights after using SMOTE

It’s important to mention is that we applied SMOTE only to the training data set, not the test data set. A detailed blog by Nick Becker guided our choices in the notebook.

At this point, we used the Random Forest Classifier for our model. It did the best when we used SPSS so we used again in our notebook. We have several ideas for a second iteration of our model in order to tune it, one of which is to try multiple algorithms to see how they compare.

Since this use case deals with classification analysis, we used some of the common ways to evaluate the performance of the model: the confusion matrix, F1 score and ROC curve, among some others. Figures 5 and 6 show the results.

Figure 6 is the ROC curve from the training data set. Figure 5 shows us that the results from the training and test data sets are pretty close, which is a good indication of consistency, though we realize that with some tuning it could get better. Nevertheless, we decided that the results were still good for the purposes of our discussion in this blog, and we stopped our iterations here. We encourage readers to refine the model further or even to use other models to solve this use case.

Conclusion

This was a project to compare creating a model in IBM’s SPSS with IBM’s Watson Studio. SPSS offers a no-code experience while Watson Studio offers the best of open-source coding capability with many IBM value adds. SPSS is an amazing product and gets better with every release, adding many new capabilities.

IBM’s Watson Studio is a great platform for both the beginning and experienced data scientist. Anyone can log in and have immediate access to a managed Spark cluster with a choice of a Jupyter notebook front-end using Scala, Python or R, SPSS and visual data modeler (no coding). It offers easy collaboration with other users, including adding other data scientists who could then look over our shoulders and make suggestions. The community is active and has already contributed dozens of tutorials, data sets and notebooks. If we had added Watson Machine Learning, we could very easily have deployed and managed our model with an instant REST endpoint to call from any application. If our data was changing, we could have WML review our model periodically and retrain it with any new data if our metric (ROC Curve) value fell below a given threshold. That, along with new data cataloging and data refinery tooling added recently, make this a platform worth checking out for any data science project.

SPSS has a lot, but not everything. Writing the python code in a notebook was a bit more time-consuming than what we did in SPSS, but it also gave quite a bit more flexibility and freedom. We had access to everything in the python libraries, and of course, one of the benefits of python as an open-source language is the trove of helpful examples.

I would say both platforms have their place, and neither can claim to be better for everything. Those doing data science for the first time will probably find SPSS an easier place to start given its drag-and-drop user interface. Those who have come out of school as programming wizards will want to write code, and DSX will give them a great way to do that without worrying about installing, configuring, and correctly integrating various product versions.

Resources

The IBM notebook and data that form the basis for this blog are available on Github.

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